This invention relates to electronic warfare and in particular to a method for recognizing observed electromagnetic signals emitted from modern radar systems, which are not stable and do not correspond to emitter modes.
Electronic warfare (EW) is based upon the recognition of observed electromagnetic signals, in particular, radar signals. The EW is an essential component of modem warfare by providing information about, for example, the movement of enemy planes or the launch of a rocket. Such Electronic Support (ES) functions allow, for example, surveillance of enemy forces and warning of an imminent attack. Another aspect of the EW is the Electronic Attack (EA) function such as jamming an enemy radar system in order to substantially reduce the attack capability of an enemy force. A third aspect of the EW is the Electronic Intelligence (ELINT) function that is concerned with the interception and analysis of unknown radar signals for the population of databases in order to support the ES and EA functions. The ELINT function is important for ES and EA tactical systems, since these systems encounter in the field radar signals emitted from unknown radar systems, or yet unknown signals emitted from known radar systems.
In current EW systems, radar signals are recognized using histograms of observed pulses in a parametric spacexe2x80x94for example, frequency, pulse width, angle of arrivalxe2x80x94and temporal periodicities in the pulse train.
Andersen et al. describe in U.S. Pat. No. 5,583,505 issued Dec. 10, 1996 a radar pulse detection and classification system that receives times-of-arrival of pulses from simultaneous emitters, deinterleaves them into bands of pulse repetition intervals, and determines the pulse periodicities using autocorrelation.
Caschera describes in U.S. Pat. No. 5,063,385 issued Nov. 5, 1991 a memory system for histogramming the pulse descriptor word output of a radar warning receiver for quickly determining the number and types of emitters of the observed radar signals.
In U.S. Pat. No. 4,918,455 issued Apr. 17, 1990, Maier teaches deinterleaving of sequential signal pulses from unknown sources by clustering similar pulses into groups, and the use of those groups to form hypothetical pulse train models.
Dunne et al. describe in US Statutory Invention Registration H513 published Aug. 2, 1998 a tracking apparatus using multi-processor modules for predicting in real time the parametric behavior of radar signals to be jammed.
A major drawback of histogramming on parameters available on individual pulses is that the temporal relationship of the pulses is lost in the histogram. Furthermore, the periodic temporal analysis is limited by the assumption that the radar system is a cyclo-stationary source of pulses. This holds true only for simple radar systems over short periods of time.
Therefore, all these prior art EW systemsxe2x80x94using histogramming and periodic temporal analysisxe2x80x94are based upon the assumption that the observed electromagnetic signals are stable and correspond to emitter modes. Emitter modes date back to the early days of radar when an operator changed the signal by manually switching to another electrical circuit. Therefore, the prior art EW systems are ill-suited for recognizing modern xe2x80x9cdynamicxe2x80x9d radar systems. For example, in response to various events modern radar systems change their emitted signal, which is automatically adjusted using a processor to maximize radar performance. The signals are no longer stable and do no longer correspond to emitter modes. Events causing a change of the emitted signal are, for example, selection of a different range display by an operator, detection of a target by the radar system and subsequently changing from a search to a tracking signal, switching between a number of periodic signal patterns to reduce blind ranges and speeds, and, launching of a missile triggering the transmission of a guidance signal from the radar system.
It is, therefore, an object of the invention to overcome the drawbacks of the prior art by providing a method capable of recognizing observed electromagnetic signals, which are not stable and do not correspond to emitter modes.
It is further an object of the invention to provide flexibility in the modeling of the radar system based upon the sensed signals and to preserve a maximum of information provided by the sensed signals.
The new method according to the invention provides the capability for recognizing modern radar systems. Describing the radar system as a finite state automaton and transforming it into a hidden Markov model provides flexibility and preserves a maximum of information provided by the observed signals. The new method is compatible with conventional receiver front-ends and allows integration into a wide range of legacy ES, EA and ELINT systems. The only hardware requirement is a fast processor with sufficient memory.
In accordance with the present invention there is provided a method for identifying a source of electromagnetic signals comprising the steps of:
receiving an electromagnetic signal emitted from the source;
providing a finite state automaton for modeling the source, the finite state automaton comprising a finite set of states and a set of transitions from state to state that occur in dependence upon an input signal, the finite state automaton for producing a sequence of output symbols from an output alphabet in dependence upon the state transitions, such that the sequence of output symbols corresponds to the received electromagnetic signal emitted from the source;
hidden Markov modeling of the finite state automaton and determining parameters of the hidden Markov model such that a sequence of observation symbols produced from an observation alphabet by the hidden Markov model is equal to the sequence of output symbols; and,
identifying the source in dependence upon the determined parameters of the hidden Markov model.
In accordance with an aspect of the present invention there is provided a method for classifying source models in dependence upon an electromagnetic signal emitted from a source of electromagnetic signals comprising the steps of:
receiving the electromagnetic signal emitted from the source;
providing a plurality of L source models xcex such that the L source models xcex have L different observation alphabets comprising symbols being integer multiples of L different time periods, the source models xcex being hidden Markov modeled finite state automatons;
transforming the received electromagnetic signal into L sequences of observation symbols O using the L different observation alphabets; and,
determining for each combination of a source model xcex of the L source models with a sequence of observation symbols O(l) of the L sequences of observation symbols an observation probability P└xcex(l)|O(l)┘, 1xe2x89xa6lxe2x89xa6L of the source model xcex for producing the sequence of observation symbols O(l).
In accordance with the aspect of the present invention there is further provided a method for decoding an electromagnetic signal emitted from a source of electromagnetic signals comprising the steps of:
receiving the electromagnetic signal emitted from the source;
determining a sequence of observation symbols O in dependence upon the received electromagnetic signal;
providing a source model xcex, the source model xcex being a hidden Markov modeled finite state automaton;
determining a plurality of sequences of state transitions Q of the hidden Markov modeled finite state automaton; and,
determining for each of the sequences of state transitions Q a probability of occurrence P[Q|O,xcex] with respect to the sequence of observation symbols O.
In accordance with the aspect of the present invention there is yet further provided a method for predicting a second portion of an electromagnetic signal based upon a first portion of the electromagnetic signal emitted from a source of electromagnetic signals comprising the steps of:
receiving the first portion of the electromagnetic signal;
determining a partial sequence of observation symbols Ot in dependence upon the observed first portion of the signal;
providing a source model, the source model being a hidden Markov modeled finite state automaton;
determining for each observation symbol of an observation alphabet of the source model a probability for being the next symbol at time t+1 in the sequence of observation symbols O based on a state transition probability distribution at time t of the source model and an observation symbol probability distribution of the source model at time t; and,
determining a most probable observation symbol.
In accordance with the aspect of the present invention there is yet further provided a method for training a source model of a source emitting electromagnetic signals comprising the steps of:
receiving an electromagnetic signal emitted from the source;
determining a sequence of observation symbols in dependence upon the received signal emitted from the source;
providing the source model, the source model being a hidden Markov modeled finite state automaton; and,
estimating a new source model based upon the probability "xgr"t(i,j) of the source model being in state i at time t and in state j at time t+1 for the sequence of observation symbols and the probability xcex3t(i) of the source model being in state i at time t for the sequence of observation symbols.